A new data augmentation method of remote sensing dataset based on Class Activation Map

Abstract

Remote sensing image scene classification is a significant direction in the field of remote sensing research. The method based on deep learning has become the most popular method in recent years because it can realize the automatic feature extraction and classification of remote sensing images. The deep learning requires a large number of samples for training and consumes large computing resources, and the data augmentation can alleviate the problem of insufficient samples. The image manipulation is one of the most commonly methods, but it may cause the loss of key information in the image. In this paper, we proposed an improved supervised data augmentation method based on Class Activation Map (CAM) and image manipulation, and then used this method to augment the high-resolution remote sensing images of NWPU dataset. We utilized three CNNs networks to count the classification accuracy of the remote sensing images. The experimental results show that the proposed method increases the accuracy of scene classification by more than 0.4%. The CAM-based methods provide a new technical support for the scene classification of remote sensing images based on deep learning.